The present study focuses on the implementation of an in-line quantitative near infrared (NIR) spectroscopic method for determining the active content of pharmaceutical pellets. The first aim was to non ... [more ▼]

The present study focuses on the implementation of an in-line quantitative near infrared (NIR) spectroscopic method for determining the active content of pharmaceutical pellets. The first aim was to non-invasively interface a dispersive NIR spectrometer with four realistic particle streams existing in the pellets manufacturing environment. Regardless of the particle stream characteristics investigated, NIR together with principal component analysis (PCA) was able to classify the samples according to their active content. Further, one of these particle stream interfaces was non-invasively investigated with a FT-NIR spectrometer. A predictive model based on Partial Least Squares (PLS) regression was able to determine the active content of pharmaceutical pellets. The NIR method was finally validated with an external validation set for an API concentration range from 80 to 120 % of the targeted active content. The prediction error of 0.9 % (root mean standard error of prediction, RMSEP) was low, indicating the accuracy of the NIR method. The accuracy profile on the validation results, an innovative approach based on tolerance intervals, demonstrated the actual and future performance of the in-line NIR method. Accordingly, the present approach paves the way for real-time release-based quality system. [less ▲]

Introduction: Neutrophil gelatinase-associated lipocalin (NGAL) has emerged as a promising marker for the detection of acute kidney injury. This marker has been proposed for urinary measurement. However ... [more ▼]

Introduction: Neutrophil gelatinase-associated lipocalin (NGAL) has emerged as a promising marker for the detection of acute kidney injury. This marker has been proposed for urinary measurement. However, in the literature, authors indistinctly use "absolute" value or NGAL to creatinine ratio. Up to now, there are no strong arguments favouring for one. This question is of importance as this marker is sensed to be used only on urine random samples. To find an answer to this very practical matter, one approach could be to compare biological CV(intra-individual variation) of the "absolute" and ratio results. [less ▲]

We present a fully validated HPLC-UV assay for the concurrent quantification of ketoglutaric acid and hydroxymethylfurfural, a ‘targeted therapy’ composition inducing a synergistic metabolic distress to ... [more ▼]

We present a fully validated HPLC-UV assay for the concurrent quantification of ketoglutaric acid and hydroxymethylfurfural, a ‘targeted therapy’ composition inducing a synergistic metabolic distress to the tumoral microenvironment. The analytes were exclusively extracted from the biomatrix via a combined-cartridge solid phase extraction assembly. The method is based on derivatizing both analytes with 2-nitrophenylhydrazine directed to their chemically divergent but commonly occurring carbonyl function. The reaction is kinetically catalyzed. Acidifying the buffered eluate post-extraction is critical for the feasibility of the reaction. The chromatographic separation is successfully accomplished on octyl columns in less than 15 min at 330 nm using 0.028% TFAA-methanol-acetonitrile (58:32:10, v/v) as eluant. The assay was validated using the concept of accuracy profile. The selectivity of the method was demonstrated in pre- and post-dosed patients from a pilot study. Quality control samples were prepared and analyzed during the routine use of the method. Life samples collected from patients enduring oesophageal and breast carcinoma with lung metastases were monitored for ketoglutarate in a trial to correlate its plasma levels with the malignancy. [less ▲]

One of the major issues within the context of the fully automated development of chromatographic methods consists of the automated detection and identification of peaks coming from complex samples such as ... [more ▼]

One of the major issues within the context of the fully automated development of chromatographic methods consists of the automated detection and identification of peaks coming from complex samples such as multi-component pharmaceutical formulations or stability studies of these formulations. The same problem can also occur with plant materials or biological matrices. This step is thus critical and time-consuming, especially when a Design of Experiments (DOE) approach is used to generate chromatograms. The use of DOE will often maximize the changes of the analytical conditions in order to explore an experimental domain. Unfortunately, this generally provides very different and “unpredictable” chromatograms which can be difficult to interpret, thus complicating peak detection and peak tracking (i.e. matching peaks among all the chromatograms). In this context, Independent Components Analysis (ICA), a new statistically based signal processing methods was investigated to solve this problem. The ICA principle assumes that the observed signal is the resultant of several phenomena (known as sources) and that all these sources are statistically independent. Under those assumptions, ICA is able to recover the sources which will have a high probability of representing the constitutive components of a chromatogram. In the present study, ICA was successfully applied for the first time to HPLC–UVDAD chromatograms and it was shown that ICA allows differentiation of noise and artifact components from those of interest by applying clustering methods based on high-order statistics computed on these components. Furthermore, on the basis of the described numerical strategy, itwas also possible to reconstruct a cleaned chromatogram with minimum influence of noise and baseline artifacts. This can present a significant advance towards the objective of providing helpful tools for the automated development of liquid chromatography (LC) methods. It seems that analytical investigations could be shortened when using this type of methodologies. [less ▲]